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Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells

An Author Correction to this article was published on 03 February 2020


Genome-wide transcriptome analyses are routinely used to monitor tissue-, disease- and cell type–specific gene expression, but it has been technically challenging to generate expression profiles from single cells. Here we describe a robust mRNA-Seq protocol (Smart-Seq) that is applicable down to single cell levels. Compared with existing methods, Smart-Seq has improved read coverage across transcripts, which enhances detailed analyses of alternative transcript isoforms and identification of single-nucleotide polymorphisms. We determined the sensitivity and quantitative accuracy of Smart-Seq for single-cell transcriptomics by evaluating it on total RNA dilution series. We found that although gene expression estimates from single cells have increased noise, hundreds of differentially expressed genes could be identified using few cells per cell type. Applying Smart-Seq to circulating tumor cells from melanomas, we identified distinct gene expression patterns, including candidate biomarkers for melanoma circulating tumor cells. Our protocol will be useful for addressing fundamental biological problems requiring genome-wide transcriptome profiling in rare cells.

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Figure 1: Smart-Seq read coverage across transcripts.
Figure 2: Sensitivity and variability in Smart-Seq from few or single cells.
Figure 3: Transcriptional and post-transcriptional analyses of cancer cell line cells using Smart-Seq.
Figure 4: Single-cell transcriptomes of circulating tumor cells.

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  1. Mortazavi, A., Williams, B., McCue, K., Schaeffer, L. & Wold, B. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat. Methods 5, 621–628 (2008).

    Article  CAS  Google Scholar 

  2. Guttman, M. et al. Ab initio reconstruction of cell type–specific transcriptomes in mouse reveals the conserved multi-exonic structure of lincRNAs. Nat. Biotechnol. 28, 503–510 (2010).

    Article  CAS  Google Scholar 

  3. Trapnell, C. et al. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat. Biotechnol. 28, 511–515 (2010).

    Article  CAS  Google Scholar 

  4. Wang, E.T. et al. Alternative isoform regulation in human tissue transcriptomes. Nature 456, 470–476 (2008).

    Article  CAS  Google Scholar 

  5. Pan, Q., Shai, O., Lee, L.J., Frey, B.J. & Blencowe, B.J. Deep surveying of alternative splicing complexity in the human transcriptome by high-throughput sequencing. Nat. Genet. 40, 1413–1415 (2008).

    Article  CAS  Google Scholar 

  6. Kurimoto, K. et al. An improved single-cell cDNA amplification method for efficient high-density oligonucleotide microarray analysis. Nucleic Acids Res. 34, e42 (2006).

    Article  Google Scholar 

  7. Tang, F. et al. mRNA-Seq whole-transcriptome analysis of a single cell. Nat. Methods 6, 377–382 (2009).

    Article  CAS  Google Scholar 

  8. Tang, F. et al. Tracing the derivation of embryonic stem cells from the inner cell mass by single-cell RNA-Seq analysis. Cell Stem Cell 6, 468–478 (2010).

    Article  CAS  Google Scholar 

  9. Islam, S. et al. Characterization of the single-cell transcriptional landscape by highly multiplex RNA-seq. Genome Res. 21, 1160–1167 (2011).

    Article  CAS  Google Scholar 

  10. Iscove, N.N. et al. Representation is faithfully preserved in global cDNA amplified exponentially from sub-picogram quantities of mRNA. Nat. Biotechnol. 20, 940–943 (2002).

    Article  CAS  Google Scholar 

  11. Katz, Y., Wang, E.T., Airoldi, E.M. & Burge, C.B. Analysis and design of RNA sequencing experiments for identifying isoform regulation. Nat. Methods 7, 1009–1015 (2010).

    Article  CAS  Google Scholar 

  12. Talasaz, A.H. et al. Isolating highly enriched populations of circulating epithelial cells and other rare cells from blood using a magnetic sweeper device. Proc. Natl. Acad. Sci. USA 106, 3970–3975 (2009).

    Article  CAS  Google Scholar 

  13. Shukla, S. et al. CTCF-promoted RNA polymerase II pausing links DNA methylation to splicing. Nature 3, 74–79 (2011).

    Article  Google Scholar 

  14. Jungbluth, A.A. et al. Expression of melanocyte-associated markers gp-100 and Melan-A/MART-1 in angiomyolipomas. An immunohistochemical and rt-PCR analysis. Virchows Arch. 434, 429–435 (1999).

    Article  CAS  Google Scholar 

  15. Tomita, Y., Montague, P.M. & Hearing, V.J. Anti-T4-tyrosinase monoclonal antibodies–specific markers for pigmented melanocytes. J. Invest. Dermatol. 85, 426–430 (1985).

    Article  CAS  Google Scholar 

  16. Fang, D. & Setaluri, V. Role of microphthalmia transcription factor in regulation of melanocyte differentiation marker TRP-1. Biochem. Biophys. Res. Commun. 256, 657–663 (1999).

    Article  CAS  Google Scholar 

  17. Chomez, P. et al. An overview of the MAGE gene family with the identification of all human members of the family. Cancer Res. 61, 5544–5551 (2001).

    CAS  PubMed  Google Scholar 

  18. Tang, A. et al. E-cadherin is the major mediator of human melanocyte adhesion to keratinocytes in vitro. J. Cell Sci. 107, 983–992 (1994).

    CAS  PubMed  Google Scholar 

  19. Duncan, L.M. et al. Down-regulation of the novel gene melastatin correlates with potential for melanoma metastasis. Cancer Res. 58, 1515–1520 (1998).

    CAS  PubMed  Google Scholar 

  20. Gudbjartsson, D.F. et al. ASIP and TYR pigmentation variants associate with cutaneous melanoma and basal cell carcinoma. Nat. Genet. 40, 886–891 (2008).

    Article  CAS  Google Scholar 

  21. Langmead, B., Trapnell, C., Pop, M. & Salzberg, S.L. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 10, R25 (2009).

    Article  Google Scholar 

  22. Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    Article  Google Scholar 

  23. Ramsköld, D., Wang, E.T., Burge, C.B. & Sandberg, R. An abundance of ubiquitously expressed genes revealed by tissue transcriptome sequence data. PLOS Comput. Biol. 5, e1000598 (2009).

    Article  Google Scholar 

  24. Bengtsson, M., Ståhlberg, A., Rorsman, P. & Kubista, M. Gene expression profiling in single cells from the pancreatic islets of Langerhans reveals lognormal distribution of mRNA levels. Genome Res. 15, 1388–1392 (2005).

    Article  CAS  Google Scholar 

  25. Au, K.F., Jiang, H., Lin, L., Xing, Y. & Wong, W.H. Detection of splice junctions from paired-end RNA-seq data by SpliceMap. Nucleic Acids Res. 38, 4570–4578 (2010).

    Article  CAS  Google Scholar 

  26. Bullard, J.H., Purdom, E., Hansen, K.D. & Dudoit, S. Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments. BMC Bioinformatics 11, 94 (2010).

    Article  Google Scholar 

  27. Sam, L.T. et al. A comparison of single molecule and amplification based sequencing of cancer transcriptomes. PLoS ONE 6, e17305 (2011).

    Article  CAS  Google Scholar 

  28. Wall, M.E., Dyck, P.A. & Brettin, T.S. SVDMAN–singular value decomposition analysis of microarray data. Bioinformatics 17, 566–568 (2001).

    Article  CAS  Google Scholar 

  29. Berger, M.F. et al. Integrative analysis of the melanoma transcriptome. Genome Res. 20, 413–427 (2010).

    Article  CAS  Google Scholar 

  30. Zawada, A.M. et al. SuperSAGE evidence for CD14.CD16+ monocytes as a third monocyte subset. Blood 118, e50–e61 (2011).

    Article  CAS  Google Scholar 

  31. Bernstein, B.E. et al. The NIH roadmap epigenomics mapping consortium. Nat. Biotechnol. 28, 1045–1048 (2010).

    Article  CAS  Google Scholar 

  32. Allison, D.B., Cui, X., Page, G.P. & Sabripour, M. Microarray data analysis: from disarray to consolidation and consensus. Nat. Rev. Genet. 7, 55–65 (2006).

    Article  CAS  Google Scholar 

  33. Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).

    Article  CAS  Google Scholar 

  34. McKenna, A. et al. The genome analysis toolkit: a mapreduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).

    Article  CAS  Google Scholar 

  35. Sherry, S.T., Ward, M. & Sirotkin, K. dbSNP-database for single nucleotide polymorphisms and other classes of minor genetic variation. Genome Res. 9, 677–679 (1999).

    CAS  PubMed  Google Scholar 

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We thank C. Burge and G. Winberg for critical reading of the manuscript, T. Juarez and J. Cotton at the University of California San Diego for their help in Internal Review Board protocol preparation and aquisition of clinical samples, A.A. Talasaz and G. Cann for assistance with the Magsweeper, members of the Science for Life laboratory (Stockholm) for assistance with MiSeq sequencer. Y.-C.W. was supported by a fellowship from the Marie Mayer Foundation. L.C.L. was supported by US National Institutes of Health (NIH) K12HD001259. J.F.L. was supported by NIH R33MH87925 and California Institute for Regenerative Medicine (CL1-00502, RT1-01108, TR1-01250, and RN2-00931). R.S. was supported by European Research Council (starting grant 243066), Swedish Research Council (2008-4562), Foundation for Strategic Research (FFL4) and Åke Wiberg Foundation (756194131).

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Authors and Affiliations



D.R. designed and performed the computational analyses of sequencing reads, prepared figures, tables and methods, and contributed manuscript text. S.L. and R.L. developed protocols and created libraries. I.K. and S.L. did primary data analysis. Y.-C.W., G.A.D. and J.F.L. prepared melanoma circulating tumor cells, melanocytes and melanoma cell line cells. O.R.F. and Q.D. contributed additional sequencing libraries. L.C.L. and G.P.S. contributed to study design and manuscript text. R.S. designed the study and prepared the manuscript, with input from other authors.

Corresponding author

Correspondence to Rickard Sandberg.

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Competing interests

S.L., R.L., I.K. and G.P.S. are employees and shareholders of Illumina.

Supplementary information

Supplementary Text and Figures

Supplementary Figs. 1–11 (PDF 1379 kb)

Supplementary Table 1

List of Smart-Seq and standard mRNA-Seq data generated (XLS 42 kb)

Supplementary Table 2

List of studies reporting total RNA amount per cell for different mammalian cell types (XLS 13 kb)

Supplementary Table 3

List of exons with significantly different inclusion levels in cancer cell line cells (XLS 42 kb)

Supplementary Table 4

Differentially expressed genes between circulating tumor cells, primary melanocytes and melanoma cell lines (XLS 5249 kb)

Supplementary Table 5

Functional categories enriched among differentially expressed genes (XLS 15 kb)

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Ramsköld, D., Luo, S., Wang, YC. et al. Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells. Nat Biotechnol 30, 777–782 (2012).

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